LangChain & LangGraph Agents Workshop for Building Stateful Agent Applications
Build a stateful graph-based agent application with tools, persistence, routing, human intervention, evaluation, tracing, failure handling, and production-oriented controls.
LangChain & LangGraph Agents for real-world execution
Build controlled AI-agent applications with current LangChain and LangGraph patterns for tools, state, graph execution, persistence, human intervention, testing, evaluation, observability, and production operations.
The workshop focuses on a functioning, observable graph-based agent system and the engineering judgment around state, routing, tools, human control, evaluation, and production behavior.
What you should be able to do when you finish
The point is not to collect AI vocabulary. It is to leave with a repeatable way to design, test, and improve a useful system.
Decide when LangChain, LangGraph, or a simpler implementation best fits an agent requirement.
Model state, nodes, edges, routing, tools, persistence, and control flow for a real agent application.
Add human review, interrupts, retries, recovery, and bounded actions where consequences matter.
Test graph behavior and agent outputs with representative scenarios and explicit evaluation criteria.
Use traces and observability to diagnose failures, latency, cost, and unexpected control flow.
Build and defend an end-to-end LangChain/LangGraph agent application and operational handoff.
A practical curriculum without the filler
Each module moves from understanding to applied system design, testing, feedback, and stronger execution.
Current LangChain and LangGraph roles, architecture, and project boundaries
Decide whether LangChain, LangGraph, or a simpler implementation fits the problem, then define the graph/system boundary.
State, graphs, nodes, routing, persistence, and context
Model state, nodes, edges, routing, persistence, and context so control flow is explicit and inspectable.
Tools, actions, subgraphs, agents, and component contracts
Connect tools, subgraphs, and agents through clear component contracts rather than implicit magic.
Human intervention, retries, recovery, and safe control flow
Add interrupts, human review, retries, fallbacks, and recovery around risky or uncertain behavior.
Testing, evaluation, tracing, observability, cost, and deployment
Test graph paths and agent outputs, use traces to diagnose behavior, and evaluate reliability, latency, and cost.
Build and ship a complete LangChain/LangGraph agent application
Build and ship a complete stateful agent application with an operational handoff and maintenance plan.
A workshop where the build is the lesson
This page is built for people searching for focused, artifact-intensive practice. The experience centers on doing the work and leaving with a concrete system, workflow, or implementation artifact.
- Focused scope
- Hands-on build work
- Immediate feedback
- Revision in context
- A finished artifact or functioning system
Learn → build → test → improve
The course content matters, but the real product is learning how to turn AI into a system that survives contact with actual work.
Learn the system
Understand the pattern, boundaries, tradeoffs, and failure modes without drowning in abstract theory.
Build the design
Apply the idea to a realistic workflow or agent with explicit inputs, rules, tools, actions, and outcomes.
Test what fails
Use representative cases, verification, and failure analysis instead of treating one successful demo as proof.
Improve the system
Revise the design until it is clearer, safer, more reliable, more observable, and better suited to real use.
Built for people who want AI to become useful inside real work
This learning path works best for people who want to move beyond one-off demos and build LangChain and LangGraph agent implementation with clear structure, controls, and outcomes.
Finish with decisions and artifacts you can reuse
AI Agentic School is designed around practical work. The exact outputs differ by product, but the goal is consistent: leave with stronger systems, clearer reasoning, and evidence of what you built and tested.
Learn → build → test → revise → apply.
LangChain & LangGraph Agents Workshop FAQ
What will I learn in this langchain & langgraph agents workshop?
You will build practical LangChain and LangGraph agent implementation skills through structured lessons, system-level examples, applied exercises, testing, feedback, and concrete implementation decisions. The emphasis is on useful real-world execution rather than prompt tricks.
Is this langchain & langgraph agents workshop suitable for beginners?
Yes, but it is action-oriented. The course begins with the core system concepts and then moves into increasingly realistic decisions, constraints, testing, and implementation thinking. Prior AI experience can help, but it is not required to understand the learning path.
Is this course tied to one AI vendor or tool?
No. AI Agentic School teaches durable system principles first. Current tools and vendor capabilities may be used as examples when useful, but vendor-specific behavior is treated as vendor-specific and can change over time.
How is AI Agentic School different from a prompt course?
Prompts are one component of an AI system. These courses focus on the wider architecture: inputs, context, state, rules, tools, actions, permissions, verification, testing, failure handling, observability, and improvement.
Can I use these skills for business or client work?
Yes. The learning is designed around repeatable system decisions that can be adapted to internal processes, client workflows, product features, research systems, content operations, automation, and other real-world use cases. The course does not guarantee a specific business outcome.
Keep building the system
LangChain & LangGraph Agents Workshop for Building Stateful Agent Applications
Build the stateful graph, control tools and routing, test failure paths, inspect traces, evaluate behavior, and finish with a production-minded agent application.